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20242026
most citedNTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

3 citations · 7 across the 7 of their papers we have counts for

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16 papers · 1 filter

cs.CV2026

DPC-Net: Dual-Prior Collaborative Network for All-in-One Image Restoration

Zhaokun He, Kangbiao Shi, Axi Niu +4

All-in-One Image Restoration (AiOIR) aims to handle diverse degradations within a unified model. However, existing methods often overlook image semantics in degradation modeling an…

cs.CV2026

Towards Video Anomaly Detection from Event Streams: A Baseline and Benchmark Datasets

Peng Wu, Yuting Yan, Guansong Pang +4

Event-based vision, characterized by low redundancy, focus on dynamic motion, and inherent privacy-preserving properties, naturally fits the demands of video anomaly detection (VAD…

cs.CV2025

GMODiff: One-Step Gain Map Refinement with Diffusion Priors for HDR Reconstruction

Tao Hu, Weiyu Zhou, Yanjie Tu +4

Pre-trained Latent Diffusion Models (LDMs) have recently shown strong perceptual priors for low-level vision tasks, making them a promising direction for multi-exposure High Dynami…

cs.CV2025

NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results

Xiaoning Liu, Zongwei Wu, Florin-Alexandru Vasluianu +102

This paper presents a comprehensive review of the NTIRE 2025 Low-Light Image Enhancement (LLIE) Challenge, highlighting the proposed solutions and final outcomes. The objective of…

cs.CV2025

Text-Visual Semantic Constrained AI-Generated Image Quality Assessment

Qiang Li, Qingsen Yan, Haojian Huang +3

With the rapid advancements in Artificial Intelligence Generated Image (AGI) technology, the accurate assessment of their quality has become an increasingly vital requirement. Prev…

cs.CV2025

HVI-CIDNet+: Beyond Extreme Darkness for Low-Light Image Enhancement

Qingsen Yan, Kangbiao Shi, Yixu Feng +4

Low-Light Image Enhancement (LLIE) aims to restore vivid content and details from corrupted low-light images. However, existing standard RGB (sRGB) color space-based LLIE methods o…